Machine learning based borehole data analysis

Abstract

A method for autopicking of bedding in a well includes receiving image logs associated with the well, eliminating tool marks from the image logs, performing a grid search for (1) a vertical amplitude and (2) a horizontal shift of the bedding at plural sampling depths to obtain a predicted bedding, calculating an azimuth and a dip of the predicted bedding, and generating an image of the predicted bedding, wherein the image includes structural features of the well.

1 . A method for autopicking of bedding in a well, the method comprising:
 receiving image logs associated with the well;   eliminating tool marks from the image logs;   performing a grid search for (1) a vertical amplitude and (2) a horizontal shift of the bedding at plural sampling depths to obtain a predicted bedding;   calculating an azimuth and a dip of the predicted bedding; and   generating an image of the predicted bedding, wherein the image includes structural features of the well.   
     
     
         2 . The method of  claim 1 , further comprising:
 receiving a breakout mask associated with the well, which is a binary image with 0s representing breakout regions and 1s representing the rocks; and   removing the breakout regions from the image log with the breakout mask.   
     
     
         3 . The method of  claim 2 , wherein the step of performing comprises:
 defining a vertical window for each searched sine wave, which produces a vertical feature at each horizontal position in the well; and   calculating cosine similarities between each vertical feature and an averaged feature across all horizontal positions.   
     
     
         4 . The method of  claim 3 , wherein the step of performing further comprises:
 summing and rescaling a similarity score of searched sine waves and generating the predicted bedding.   
     
     
         5 . The method of  claim 1 , further comprising:
 polarizing the image logs to distinguish between dark and bright pixels;   applying one or more algorithms to enhance a difference between the dark and bright pixels;   generating an image with breakout regions by selecting the dark pixels, wherein the image includes structural features of the well; and   removing the breakout regions from the image logs prior to the step of performing.   
     
     
         6 . The method of  claim 5 , further comprising:
 filtering out polygons having an area smaller than a given threshold before the generating step.   
     
     
         7 . The method of  claim 5 , wherein the step of applying one or more algorithms comprises:
 strengthening a difference between the dark and bright pixels by applying an erosion algorithm that removes pixels on boundaries of regions of dark and bright pixels.   
     
     
         8 . The method of  claim 7 , wherein the step of applying one or more algorithms comprises:
 determining a contour of the regions of dark pixels by applying a contour detection algorithm and picking isolated polygons as corresponding to the regions of dark pixels.   
     
     
         9 . The method of  claim 8 , wherein the step of applying one or more algorithms comprises:
 eliminating erosion effect due to the strengthening step by applying a dilation algorithm.   
     
     
         10 . The method of  claim 9 , wherein the step of applying one or more algorithms comprises:
 merging overlapped detected polygons with a non-maximum suppression algorithm.   
     
     
         11 . The method of  claim 10 , further comprising:
 applying a depth first search algorithm to find the dark regions when there is a higher proportion of breakout regions than rock regions.   
     
     
         12 . The method of  claim 10 , wherein the dark pixels calculated with the steps of strengthening, determining, eliminating, and merging are selected for the image with breakout regions. 
     
     
         13 . A method for facies classification based on image logs associated with a log, the method comprising:
 receiving image logs associated with the well;   splitting the image logs into plural patches;   implementing a trained classifier to determine the facies corresponding to the plural patches; and   assembling the facies to obtain an image of the well, wherein the image includes structural features of the well.   
     
     
         14 . The method of  claim 13 , further comprising:
 defining facies types and associating the patches with one of the facies;   receiving breakouts regions of the well and removing tool marks and the breakout regions from the patches to obtained pre-processed data.   
     
     
         15 . The method of  claim 14 , further comprising:
 training a classifier based on the pre-processed data to obtain the trained classifier.   
     
     
         16 . The method of  claim 13 , further comprising:
 defining facies labels as being vuggy, semi-laminated, laminated, and structureless and training the classifier to determine these labels.   
     
     
         17 . The method of  claim 13 , further comprising:
 polarizing the image logs to distinguish between dark and bright pixels;   applying one or more algorithms to enhance a difference between the dark and bright pixels;   generating an image with breakout regions by selecting the dark pixels, wherein the image includes structural features of the well; and   removing the breakout regions from the image logs prior to the step of splitting.   
     
     
         18 . The method of  claim 17 , wherein the step of applying one or more algorithms comprises:
 strengthening the difference between the dark and bright pixels by applying an erosion algorithm that removes pixels on boundaries of regions of dark and bright pixels; and   determining a contour of the regions of dark pixels by applying a contour detection algorithm and picking isolated polygons as corresponding to the regions of dark pixels.   
     
     
         19 . The method of  claim 18 , wherein the step of applying one or more algorithms comprises:
 eliminating erosion effect due to the strengthening step by applying a dilation algorithm.   
     
     
         20 . The method of  claim 19 , wherein the step of applying one or more algorithms comprises:
 merging overlapped detected polygons with a non-maximum suppression algorithm.